Van Der Schaar, Mihaela

178 publications

ICLRW 2025 Decision Tree Induction with Dynamic Feature Generation: A Framework for Interpretable DNA Sequence Analysis Nicolas Huynh, Krzysztof Kacprzyk, Ryan M Sheridan, David L. Bentley, Mihaela van der Schaar

ICML 2025 G-Sim: Generative Simulations with Large Language Models and Gradient-Free Calibration Samuel Holt, Max Ruiz Luyten, Antonin Berthon, Mihaela Van Der Schaar

ICLR 2025 Going Beyond Static: Understanding Shifts with Time-Series Attribution Jiashuo Liu, Nabeel Seedat, Peng Cui, Mihaela van der Schaar

NeurIPS 2025 Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inference Harry Amad, Zhaozhi Qian, Dennis Frauen, Julianna Piskorz, Stefan Feuerriegel, Mihaela van der Schaar

ICLR 2025 No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs Krzysztof Kacprzyk, Mihaela van der Schaar

ICML 2025 Position: All Current Generative Fidelity and Diversity Metrics Are Flawed Ossi Räisä, Boris Van Breugel, Mihaela Van Der Schaar

ICML 2025 Position: Not All Explanations for Deep Learning Phenomena Are Equally Valuable Alan Jeffares, Mihaela Van Der Schaar

ICML 2025 Position: Truly Self-Improving Agents Require Intrinsic Metacognitive Learning Tennison Liu, Mihaela Van Der Schaar

ICLRW 2025 Position: What's the Next Frontier for Data-Centric AI? Data Savvy Agents! Nabeel Seedat, Jiashuo Liu, Mihaela van der Schaar

ICML 2025 Preference Learning for AI Alignment: A Causal Perspective Kasia Kobalczyk, Mihaela Van Der Schaar

ICLR 2025 Risk-Sensitive Diffusion: Robustly Optimizing Diffusion Models with Noisy Samples Yangming Li, Max Ruiz Luyten, Mihaela van der Schaar

NeurIPS 2025 Semantic-KG: Using Knowledge Graphs to Construct Benchmarks for Measuring Semantic Similarity Qiyao Wei, Edward Morrell, Lea Goetz, Mihaela van der Schaar

NeurIPS 2025 Simulating Viva Voce Examinations to Evaluate Clinical Reasoning in Large Language Models Christopher Chiu, Silviu Pitis, Mihaela van der Schaar

ICML 2025 Skip the Equations: Learning Behavior of Personalized Dynamical Systems Directly from Data Krzysztof Kacprzyk, Julianna Piskorz, Mihaela Van Der Schaar

ICLRW 2025 Skip the Equations: Learning Behavior of Personalized Dynamical Systems Directly from Data Krzysztof Kacprzyk, Julianna Piskorz, Mihaela van der Schaar

ICML 2025 Statistical Hypothesis Testing for Auditing Robustness in Language Models Paulius Rauba, Qiyao Wei, Mihaela Van Der Schaar

ICML 2025 Stochastic Encodings for Active Feature Acquisition Alexander Luke Ian Norcliffe, Changhee Lee, Fergus Imrie, Mihaela Van Der Schaar, Pietro Lio

ICML 2025 Strategic Planning: A Top-Down Approach to Option Generation Max Ruiz Luyten, Antonin Berthon, Mihaela Van Der Schaar

ICML 2025 The Synergy of LLMs & RL Unlocks Offline Learning of Generalizable Language-Conditioned Policies with Low-Fidelity Data Thomas Pouplin, Kasia Kobalczyk, Hao Sun, Mihaela Van Der Schaar

NeurIPS 2025 Timely Clinical Diagnosis Through Active Test Selection Silas Ruhrberg Estévez, Nicolás Astorga, Mihaela van der Schaar

ICLR 2025 Towards Automated Knowledge Integration from Human-Interpretable Representations Kasia Kobalczyk, Mihaela van der Schaar

ICLRW 2025 Towards Human-Guided, Data-Centric LLM Co-Pilots Evgeny Saveliev, Jiashuo Liu, Nabeel Seedat, Anders Boyd, Mihaela van der Schaar

NeurIPS 2025 Treatment Effect Estimation for Optimal Decision-Making Dennis Frauen, Valentyn Melnychuk, Jonas Schweisthal, Mihaela van der Schaar, Stefan Feuerriegel

ICML 2025 Unified Screening for Multiple Diseases Yiğit Narter, Alihan Hüyük, Mihaela Van Der Schaar, Cem Tekin

ICLR 2024 A Neural Framework for Generalized Causal Sensitivity Analysis Dennis Frauen, Fergus Imrie, Alicia Curth, Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar

NeurIPS 2024 A Theoretical Design of Concept Sets: Improving the Predictability of Concept Bottleneck Models Max Ruiz Luyten, Mihaela van der Schaar

ICLRW 2024 Actions Speak Louder than Words: Superficial Fairness Alignment in LLMs Qiyao Wei, Alex James Chan, Lea Goetz, David Watson, Mihaela van der Schaar

NeurIPS 2024 Active Learning with LLMs for Partially Observed and Cost-Aware Scenarios Nicolás Astorga, Tennison Liu, Nabeel Seedat, Mihaela van der Schaar

NeurIPS 2024 Automatically Learning Hybrid Digital Twins of Dynamical Systems Samuel Holt, Tennison Liu, Mihaela van der Schaar

NeurIPS 2024 Context-Aware Testing: A New Paradigm for Model Testing with Large Language Models Paulius Rauba, Nabeel Seedat, Max Ruiz Luyten, Mihaela van der Schaar

DMLR 2024 DMLR: Data-Centric Machine Learning Research - Past, Present and Future Luis Oala, Manil Maskey, Lilith Bat-Leah, Alicia Parrish, Nezihe Merve Gürel, Tzu-Sheng Kuo, Yang Liu, Rotem Dror, Danilo Brajovic, Xiaozhe Yao, Max Bartolo, William A Gaviria Rojas, Ryan Hileman, Rainier Aliment, Michael W. Mahoney, Meg Risdal, Matthew Lease, Wojciech Samek, Debojyoti Dutta, Curtis G Northcutt, Cody Coleman, Braden Hancock, Bernard Koch, Girmaw Abebe Tadesse, Bojan Karlaš, Ahmed Alaa, Adji Bousso Dieng, Natasha Noy, Vijay Janapa Reddi, James Zou, Praveen Paritosh, Mihaela van der Schaar, Kurt Bollacker, Lora Aroyo, Ce Zhang, Joaquin Vanschoren, Isabelle Guyon, Peter Mattson

NeurIPS 2024 Data-Driven Discovery of Dynamical Systems in Pharmacology Using Large Language Models Samuel Holt, Zhaozhi Qian, Tennison Liu, James Weatherall, Mihaela van der Schaar

NeurIPS 2024 Deep Learning Through a Telescoping Lens: A Simple Model Provides Empirical Insights on Grokking, Gradient Boosting & Beyond Alan Jeffares, Alicia Curth, Mihaela van der Schaar

ICLR 2024 Defining Expertise: Applications to Treatment Effect Estimation Alihan Hüyük, Qiyao Wei, Alicia Curth, Mihaela van der Schaar

ICML 2024 Dense Reward for Free in Reinforcement Learning from Human Feedback Alex James Chan, Hao Sun, Samuel Holt, Mihaela Van Der Schaar

ICML 2024 Discovering Features with Synergistic Interactions in Multiple Views Chohee Kim, Mihaela Van Der Schaar, Changhee Lee

NeurIPS 2024 Discovering Preference Optimization Algorithms with and for Large Language Models Chris Lu, Samuel Holt, Claudio Fanconi, Alex J. Chan, Jakob Foerster, Mihaela van der Schaar, Robert Tjarko Lange

ICMLW 2024 Discovering Preference Optimization Algorithms with and for Large Language Models Chris Lu, Samuel Holt, Claudio Fanconi, Alex James Chan, Jakob Nicolaus Foerster, Mihaela van der Schaar, Robert Tjarko Lange

ICLR 2024 Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness Characterization Methods for Data-Centric AI Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar

NeurIPSW 2024 Improving LLM Generation with Inverse and Forward Alignment: Reward Modeling, Prompting, Fine-Tuning, and Inference-Time Optimization Hao Sun, Thomas Pouplin, Nicolás Astorga, Tennison Liu, Mihaela van der Schaar

ICMLW 2024 Informed Meta-Learning Kasia Kobalczyk, Mihaela van der Schaar

ICMLW 2024 Inverse Reinforcement Learning from Demonstrations for LLM Alignment Hao Sun, Mihaela van der Schaar

ICLR 2024 L2MAC: Large Language Model Automatic Computer for Extensive Code Generation Samuel Holt, Max Ruiz Luyten, Mihaela van der Schaar

ICLR 2024 Large Language Models to Enhance Bayesian Optimization Tennison Liu, Nicolás Astorga, Nabeel Seedat, Mihaela van der Schaar

ICMLW 2024 Looking at Deep Learning Phenomena Through a Telescoping Lens Alan Jeffares, Alicia Curth, Mihaela van der Schaar

NeurIPSW 2024 Matchmaker: Self-Improving Compositional LLM Programs for Table Schema Matching Nabeel Seedat, Mihaela van der Schaar

NeurIPSW 2024 Matchmaker: Self-Improving Large Language Model Programs for Schema Matching Nabeel Seedat, Mihaela van der Schaar

ICML 2024 Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments Jonas Schweisthal, Dennis Frauen, Mihaela Van Der Schaar, Stefan Feuerriegel

ICLR 2024 ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference Krzysztof Kacprzyk, Samuel Holt, Jeroen Berrevoets, Zhaozhi Qian, Mihaela van der Schaar

ICLR 2024 On Error Propagation of Diffusion Models Yangming Li, Mihaela van der Schaar

ICML 2024 Position: Why Tabular Foundation Models Should Be a Research Priority Boris Van Breugel, Mihaela Van Der Schaar

NeurIPS 2024 Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar

ICLR 2024 Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL Hao Sun, Alihan Hüyük, Mihaela van der Schaar

ICML 2024 Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise Thomas Pouplin, Alan Jeffares, Nabeel Seedat, Mihaela Van Der Schaar

NeurIPS 2024 Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments Paulius Rauba, Nabeel Seedat, Krzysztof Kacprzyk, Mihaela van der Schaar

ICLR 2024 Soft Mixture Denoising: Beyond the Expressive Bottleneck of Diffusion Models Yangming Li, Boris van Breugel, Mihaela van der Schaar

ICML 2024 Time Series Diffusion in the Frequency Domain Jonathan Crabbé, Nicolas Huynh, Jan Pawel Stanczuk, Mihaela Van Der Schaar

ICLR 2024 Towards Transparent Time Series Forecasting Krzysztof Kacprzyk, Tennison Liu, Mihaela van der Schaar

DMLR 2024 When Is Off-Policy Evaluation (Reward Modeling) Useful in Contextual Bandits? a Data-Centric Perspective Hao Sun, Alex James Chan, Nabeel Seedat, Alihan Hüyük, Mihaela van der Schaar

DMLR 2024 You Can't Handle the (dirty) Truth: Data-Centric Insights Improve Pseudo-Labeling Nabeel Seedat, Nicolas Huynh, Fergus Imrie, Mihaela van der Schaar

NeurIPS 2023 A U-Turn on Double Descent: Rethinking Parameter Counting in Statistical Learning Alicia Curth, Alan Jeffares, Mihaela van der Schaar

NeurIPS 2023 Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of Examples Hao Sun, Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar

ICML 2023 Accounting for Informative Sampling When Learning to Forecast Treatment Outcomes over Time Toon Vanderschueren, Alicia Curth, Wouter Verbeke, Mihaela Van Der Schaar

NeurIPS 2023 Active Observing in Continuous-Time Control Samuel Holt, Alihan Hüyük, Mihaela van der Schaar

ICML 2023 Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions Alicia Curth, Alihan Hüyük, Mihaela Van Der Schaar

NeurIPS 2023 AllSim: Simulating and Benchmarking Resource Allocation Policies in Multi-User Systems Jeroen Berrevoets, Daniel Jarrett, Alex Chan, Mihaela van der Schaar

NeurIPS 2023 Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data Boris van Breugel, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar

NeurIPS 2023 D-CIPHER: Discovery of Closed-Form Partial Differential Equations Krzysztof Kacprzyk, Zhaozhi Qian, Mihaela van der Schaar

ICLR 2023 Deep Generative Symbolic Regression Samuel Holt, Zhaozhi Qian, Mihaela van der Schaar

ICML 2023 Differentiable and Transportable Structure Learning Jeroen Berrevoets, Nabeel Seedat, Fergus Imrie, Mihaela Van Der Schaar

NeurIPS 2023 Evaluating the Robustness of Interpretability Methods Through Explanation Invariance and Equivariance Jonathan Crabbé, Mihaela van der Schaar

ICLR 2023 GOGGLE: Generative Modelling for Tabular Data by Learning Relational Structure Tennison Liu, Zhaozhi Qian, Jeroen Berrevoets, Mihaela van der Schaar

ICML 2023 In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation Alicia Curth, Mihaela Van Der Schaar

NeurIPS 2023 Joint Training of Deep Ensembles Fails Due to Learner Collusion Alan Jeffares, Tennison Liu, Jonathan Crabbé, Mihaela van der Schaar

ICML 2023 Learning Representations Without Compositional Assumptions Tennison Liu, Jeroen Berrevoets, Zhaozhi Qian, Mihaela Van Der Schaar

NeurIPSW 2023 Optimising Human-AI Collaboration by Learning Convincing Explanations Alex Chan, Alihan Hüyük, Mihaela van der Schaar

NeurIPS 2023 Reimagining Synthetic Tabular Data Generation Through Data-Centric AI: A Comprehensive Benchmark Lasse Hansen, Nabeel Seedat, Mihaela van der Schaar, Andrija Petrovic

NeurIPS 2023 Risk-Averse Active Sensing for Timely Outcome Prediction Under Cost Pressure Yuchao Qin, Mihaela van der Schaar, Changhee Lee

NeurIPSW 2023 Shape Arithmetic Expressions Krzysztof Kacprzyk, Mihaela van der Schaar

NeurIPS 2023 Synthcity: A Benchmark Framework for Diverse Use Cases of Tabular Synthetic Data Zhaozhi Qian, Rob Davis, Mihaela van der Schaar

ICML 2023 Synthetic Data, Real Errors: How (Not) to Publish and Use Synthetic Data Boris Van Breugel, Zhaozhi Qian, Mihaela Van Der Schaar

ICLR 2023 TANGOS: Regularizing Tabular Neural Networks Through Gradient Orthogonalization and Specialization Alan Jeffares, Tennison Liu, Jonathan Crabbé, Fergus Imrie, Mihaela van der Schaar

NeurIPS 2023 TRIAGE: Characterizing and Auditing Training Data for Improved Regression Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar

NeurIPS 2023 What Is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization Hao Sun, Boris van Breugel, Jonathan Crabbé, Nabeel Seedat, Mihaela van der Schaar

ICLR 2023 When to Make and Break Commitments? Alihan Hüyük, Zhaozhi Qian, Mihaela van der Schaar

AISTATS 2022 Identifiable Energy-Based Representations: An Application to Estimating Heterogeneous Causal Effects Yao Zhang, Jeroen Berrevoets, Mihaela Van Der Schaar

NeurIPS 2022 Benchmarking Heterogeneous Treatment Effect Models Through the Lens of Interpretability Jonathan Crabbé, Alicia Curth, Ioana Bica, Mihaela van der Schaar

NeurIPS 2022 Composite Feature Selection Using Deep Ensembles Fergus Imrie, Alexander Norcliffe, Pietro Lió, Mihaela van der Schaar

NeurIPS 2022 Concept Activation Regions: A Generalized Framework for Concept-Based Explanations Jonathan Crabbé, Mihaela van der Schaar

NeurIPSW 2022 D-CIPHER: Discovery of Closed-Form Partial Differential Equations Krzysztof Kacprzyk, Zhaozhi Qian, Mihaela van der Schaar

ICLR 2022 D-CODE: Discovering Closed-Form ODEs from Observed Trajectories Zhaozhi Qian, Krzysztof Kacprzyk, Mihaela van der Schaar

NeurIPS 2022 Data-IQ: Characterizing Subgroups with Heterogeneous Outcomes in Tabular Data Nabeel Seedat, Jonathan Crabbé, Ioana Bica, Mihaela van der Schaar

AAAI 2022 Inferring Lexicographically-Ordered Rewards from Preferences Alihan Hüyük, William R. Zame, Mihaela van der Schaar

ICLR 2022 Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies Alex Chan, Alicia Curth, Mihaela van der Schaar

ICLR 2022 Neural Graphical Modelling in Continuous-Time: Consistency Guarantees and Algorithms Alexis Bellot, Kim Branson, Mihaela van der Schaar

NeurIPS 2022 Online Decision Mediation Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar

ICLR 2022 POETREE: Interpretable Policy Learning with Adaptive Decision Trees Alizée Pace, Alex Chan, Mihaela van der Schaar

ICLR 2022 Self-Supervision Enhanced Feature Selection with Correlated Gates Changhee Lee, Fergus Imrie, Mihaela van der Schaar

NeurIPS 2022 Synthetic Model Combination: An Instance-Wise Approach to Unsupervised Ensemble Learning Alex Chan, Mihaela van der Schaar

NeurIPS 2022 Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation Ioana Bica, Mihaela van der Schaar

MLJ 2021 CPAS: The UK's National Machine Learning-Based Hospital Capacity Planning System for COVID-19 Zhaozhi Qian, Ahmed M. Alaa, Mihaela van der Schaar

ICLR 2021 Clairvoyance: A Pipeline Toolkit for Medical Time Series Daniel Jarrett, Jinsung Yoon, Ioana Bica, Zhaozhi Qian, Ari Ercole, Mihaela van der Schaar

NeurIPS 2021 Closing the Loop in Medical Decision Support by Understanding Clinical Decision-Making: A Case Study on Organ Transplantation Yuchao Qin, Fergus Imrie, Alihan Hüyük, Daniel Jarrett, Alexander Gimson, Mihaela van der Schaar

NeurIPS 2021 Conformal Time-Series Forecasting Kamile Stankeviciute, Ahmed M. Alaa, Mihaela van der Schaar

NeurIPS 2021 DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks Boris van Breugel, Trent Kyono, Jeroen Berrevoets, Mihaela van der Schaar

NeurIPS 2021 Estimating Multi-Cause Treatment Effects via Single-Cause Perturbation Zhaozhi Qian, Alicia Curth, Mihaela van der Schaar

NeurIPS 2021 Explaining Latent Representations with a Corpus of Examples Jonathan Crabbe, Zhaozhi Qian, Fergus Imrie, Mihaela van der Schaar

ICML 2021 Explaining Time Series Predictions with Dynamic Masks Jonathan Crabbé, Mihaela Van Der Schaar

ICLR 2021 Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning Alihan Hüyük, Daniel Jarrett, Cem Tekin, Mihaela van der Schaar

ICLR 2021 Generative Time-Series Modeling with Fourier Flows Ahmed Alaa, Alex James Chan, Mihaela van der Schaar

MLJ 2021 How Artificial Intelligence and Machine Learning Can Help Healthcare Systems Respond to COVID-19 Mihaela van der Schaar, Ahmed M. Alaa, R. Andres Floto, Alexander Gimson, Stefan Scholtes, Angela M. Wood, Eoin F. McKinney, Daniel Jarrett, Pietro Lió, Ari Ercole

NeurIPS 2021 Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression Zhaozhi Qian, William Zame, Lucas Fleuren, Paul Elbers, Mihaela van der Schaar

NeurIPS 2021 Invariant Causal Imitation Learning for Generalizable Policies Ioana Bica, Daniel Jarrett, Mihaela van der Schaar

ICML 2021 Inverse Decision Modeling: Learning Interpretable Representations of Behavior Daniel Jarrett, Alihan Hüyük, Mihaela Van Der Schaar

ICLR 2021 Learning "What-If" Explanations for Sequential Decision-Making Ioana Bica, Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar

NeurIPS 2021 MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms Trent Kyono, Yao Zhang, Alexis Bellot, Mihaela van der Schaar

NeurIPS 2021 On Inductive Biases for Heterogeneous Treatment Effect Estimation Alicia Curth, Mihaela van der Schaar

ICLR 2021 Scalable Bayesian Inverse Reinforcement Learning Alex James Chan, Mihaela van der Schaar

NeurIPS 2021 SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data Alicia Curth, Changhee Lee, Mihaela van der Schaar

NeurIPS 2021 SyncTwin: Treatment Effect Estimation with Longitudinal Outcomes Zhaozhi Qian, Yao Zhang, Ioana Bica, Angela Wood, Mihaela van der Schaar

NeurIPS 2021 Time-Series Generation by Contrastive Imitation Daniel Jarrett, Ioana Bica, Mihaela van der Schaar

NeurIPS 2020 CASTLE: Regularization via Auxiliary Causal Graph Discovery Trent Kyono, Yao Zhang, Mihaela van der Schaar

ICML 2020 Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions Ahmed Alaa, Mihaela Van Der Schaar

ICLR 2020 Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations Ioana Bica, Ahmed M. Alaa, James Jordon, Mihaela van der Schaar

NeurIPS 2020 Estimating the Effects of Continuous-Valued Interventions Using Generative Adversarial Networks Ioana Bica, James Jordon, Mihaela van der Schaar

ICML 2020 Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions Ahmed Alaa, Mihaela Van Der Schaar

NeurIPS 2020 Gradient Regularized V-Learning for Dynamic Treatment Regimes Yao Zhang, Mihaela van der Schaar

ICLR 2020 Individualised Dose-Response Estimation Using Generative Adversarial Nets Ioana Bica, James Jordon, Mihaela van der Schaar

ICML 2020 Inverse Active Sensing: Modeling and Understanding Timely Decision-Making Daniel Jarrett, Mihaela Van Der Schaar

NeurIPS 2020 Learning Outside the Black-Box: The Pursuit of Interpretable Models Jonathan Crabbe, Yao Zhang, William Zame, Mihaela van der Schaar

ICML 2020 Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints Cong Shen, Zhiyang Wang, Sofia Villar, Mihaela Van Der Schaar

NeurIPS 2020 OrganITE: Optimal Transplant Donor Organ Offering Using an Individual Treatment Effect Jeroen Berrevoets, James Jordon, Ioana Bica, Alexander Gimson, Mihaela van der Schaar

NeurIPS 2020 Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification Hyun-Suk Lee, Yao Zhang, William Zame, Cong Shen, Jang-Won Lee, Mihaela van der Schaar

NeurIPS 2020 Strictly Batch Imitation Learning by Energy-Based Distribution Matching Daniel Jarrett, Ioana Bica, Mihaela van der Schaar

ICLR 2020 Target-Embedding Autoencoders for Supervised Representation Learning Daniel Jarrett, Mihaela van der Schaar

ICML 2020 Temporal Phenotyping Using Deep Predictive Clustering of Disease Progression Changhee Lee, Mihaela Van Der Schaar

ICML 2020 Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders Ioana Bica, Ahmed Alaa, Mihaela Van Der Schaar

ICML 2020 Unlabelled Data Improves Bayesian Uncertainty Calibration Under Covariate Shift Alex Chan, Ahmed Alaa, Zhaozhi Qian, Mihaela Van Der Schaar

NeurIPS 2020 VIME: Extending the Success of Self- and Semi-Supervised Learning to Tabular Domain Jinsung Yoon, Yao Zhang, James Jordon, Mihaela van der Schaar

NeurIPS 2020 When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment Using Compartmental Gaussian Processes Zhaozhi Qian, Ahmed M. Alaa, Mihaela van der Schaar

NeurIPS 2019 Attentive State-Space Modeling of Disease Progression Ahmed M. Alaa, Mihaela van der Schaar

NeurIPS 2019 Conditional Independence Testing Using Generative Adversarial Networks Alexis Bellot, Mihaela van der Schaar

MLJ 2019 Constructing Effective Personalized Policies Using Counterfactual Inference from Biased Data Sets with Many Features Onur Atan, William R. Zame, Qiaojun Feng, Mihaela van der Schaar

NeurIPS 2019 Demystifying Black-Box Models with Symbolic Metamodels Ahmed M. Alaa, Mihaela van der Schaar

NeurIPS 2019 Differentially Private Bagging: Improved Utility and Cheaper Privacy than Subsample-and-Aggregate James Jordon, Jinsung Yoon, Mihaela van der Schaar

ICLR 2019 INVASE: Instance-Wise Variable Selection Using Neural Networks Jinsung Yoon, James Jordon, Mihaela van der Schaar

ICLR 2019 KnockoffGAN: Generating Knockoffs for Feature Selection Using Generative Adversarial Networks James Jordon, Jinsung Yoon, Mihaela van der Schaar

ICLR 2019 PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees James Jordon, Jinsung Yoon, Mihaela van der Schaar

NeurIPS 2019 Time-Series Generative Adversarial Networks Jinsung Yoon, Daniel Jarrett, Mihaela van der Schaar

ICML 2019 Validating Causal Inference Models via Influence Functions Ahmed Alaa, Mihaela Van Der Schaar

JMLR 2018 A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference Ahmed M. Alaa, Mihaela van der Schaar

MLHC 2018 Boosted Trees for Risk Prognosis Alexis Bellot, Mihaela van der Schaar

ICLR 2018 Deep Sensing: Active Sensing Using Multi-Directional Recurrent Neural Networks Jinsung Yoon, William R. Zame, Mihaela van der Schaar

AAAI 2018 Deep-Treat: Learning Optimal Personalized Treatments from Observational Data Using Neural Networks Onur Atan, James Jordon, Mihaela van der Schaar

AAAI 2018 DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks Changhee Lee, William R. Zame, Jinsung Yoon, Mihaela van der Schaar

MLHC 2018 Disease-Atlas: Navigating Disease Trajectories Using Deep Learning Bryan Lim, Mihaela van der Schaar

ICLR 2018 GANITE: Estimation of Individualized Treatment Effects Using Generative Adversarial Nets Jinsung Yoon, James Jordon, Mihaela van der Schaar

NeurIPS 2018 Multitask Boosting for Survival Analysis with Competing Risks Alexis Bellot, Mihaela van der Schaar

AISTATS 2018 Tree-Based Bayesian Mixture Model for Competing Risks Alexis Bellot, Mihaela van der Schaar

NeurIPS 2017 Bayesian Inference of Individualized Treatment Effects Using Multi-Task Gaussian Processes Ahmed M. Alaa, Mihaela van der Schaar

NeurIPS 2017 DPSCREEN: Dynamic Personalized Screening Kartik Ahuja, William Zame, Mihaela van der Schaar

NeurIPS 2017 Deep Multi-Task Gaussian Processes for Survival Analysis with Competing Risks Ahmed M. Alaa, Mihaela van der Schaar

AAAI 2017 Personalized Donor-Recipient Matching for Organ Transplantation Jinsung Yoon, Ahmed M. Alaa, Martin Cadeiras, Mihaela van der Schaar

AAAI 2017 Progressive Prediction of Student Performance in College Programs Jie Xu, Yuli Han, Daniel Marcu, Mihaela van der Schaar

NeurIPS 2016 A Non-Parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics William Hoiles, Mihaela van der Schaar

NeurIPS 2016 Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition Ahmed M. Alaa, Mihaela van der Schaar

MLJ 2016 Context-Based Unsupervised Ensemble Learning and Feature Ranking Erfan Soltanmohammadi, Mort Naraghi-Pour, Mihaela van der Schaar

AISTATS 2015 Global Multi-Armed Bandits with Hölder Continuity Onur Atan, Cem Tekin, Mihaela van der Schaar

NeurIPS 2014 Discovering, Learning and Exploiting Relevance Cem Tekin, Mihaela van der Schaar